DINAMIKA HUBUNGAN FOREIGN DIRECT INVESTMENT (FDI), STABILITAS MAKROEKONOMI DAN RETURN INDEKS SAHAM SYARIAH DI EMPAT NEGARA ASEAN
Bibliographic record
Abstract
Southeast Asian countries are looking forward to capital market \nintegration. The presence of this momentum requires stable economic conditions \nin each country and an attractive capital market. This momentum is also an \nopportunity for the Islamic capital market to be further developed in this region. \nThis study aims to examine the effect of Foreign Direct Investment (FDI) and \nmacroeconomic variables, namely economic growth, inflation, reference interest \nrates and exchange rates on the return of the Islamic stock index in four ASEAN \ncountries, namely Indonesia, Malaysia, Thailand and Singapore. The research \nperiod since four quarter of 2006 until the first quarter of 2020. The method used \nin empirical evidence in this study is the Autoregressive Distributed Lag Bounds \nTesting Approach (ARDL). This study found a long-term cointegration \nrelationship in all research object countries. In terms of long-term relationships \nand short-term dynamics, this study finds variations in yield and direction \ncoefficients in 4 ASEAN countries. The speed of readjustment of balance in case \nof shocks, respectively, is 44.7%, 65.4%, 43.5% and 50.0% per month. \n \nARDL
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".